code: def create_summary_numeric_group_data(
      feature_dict: dict, compare_dict: dict
  ):
      stats = feature_dict["stats"]
  
      def get_compare(stat_name):
          if compare_dict is not None:
              return compare_dict["stats"][stat_name]
          return 0
  
      group_1 = list()
      group_1.append(
          {
              "name": "MAX",
              "value": stats["max"],
              "compare_value": get_compare("max"),
          }
      )
      group_1.append(
          {
              "name": "95%",
              "value": stats["perc95"],
              "compare_value": get_compare("perc95"),
          }
      )
      group_1.append(
          {
              "name": "Q3",
              "value": stats["perc75"],
              "compare_value": get_compare("perc75"),
          }
      )
      if stats["mean"] > stats["perc50"]:
          group_1.append(
              {
                  "name": "AVG",
                  "value": stats["mean"],
                  "compare_value": get_compare("mean"),
              }
          )
          group_1.append(
              {
                  "name": "MEDIAN",
                  "value": stats["perc50"],
                  "compare_value": get_compare("perc50"),
              }
          )
      else:
          group_1.append(
              {
                  "name": "MEDIAN",
                  "value": stats["perc50"],
                  "compare_value": get_compare("perc50"),
              }
          )
          group_1.append(
              {
                  "name": "AVG",
                  "value": stats["mean"],
                  "compare_value": get_compare("mean"),
              }
          )
      group_1.append(
          {
              "name": "Q1",
              "value": stats["perc25"],
              "compare_value": get_compare("perc25"),
          }
      )
      group_1.append(
          {
              "name": "5%",
              "value": stats["perc5"],
              "compare_value": get_compare("perc5"),
          }
      )
      group_1.append(
          {
              "name": "MIN",
              "value": stats["min"],
              "compare_value": get_compare("min"),
          }
      )
  
      group_2 = list()
      group_2.append(
          {
              "name": "RANGE",
              "value": stats["range"],
              "compare_value": get_compare("range"),
          }
      )
      group_2.append(
          {
              "name": "IQR",
              "value": stats["iqr"],
              "compare_value": get_compare("iqr"),
          }
      )
      group_2.append(
          {
              "name": "STD",
              "value": stats["std"],
              "compare_value": get_compare("std"),
          }
      )
      group_2.append(
          {
              "name": "VAR",
              "value": stats["variance"],
              "compare_value": get_compare("variance"),
          }
      )
      group_2.append({"name": "", "value": "", "compare_value": ""})
      group_2.append(
          {
              "name": "KURT.",
              "value": stats["kurtosis"],
              "compare_value": get_compare("kurtosis"),
          }
      )
      group_2.append(
          {
              "name": "SKEW",
              "value": stats["skewness"],
              "compare_value": get_compare("skewness"),
          }
      )
      group_2.append(
          {
              "name": "SUM",
              "value": stats["sum"],
              "compare_value": get_compare("sum"),
          }
      )
      return group_1, group_2
  
imports: import numpy
  import html
  from operator import itemgetter
  from jinja2 import Environment, PackageLoader
  import sweetviz.sv_html_formatters
  from sweetviz.config import config
  from sweetviz.sv_types import NumWithPercent, FeatureType, OTHERS_GROUPED
  from sweetviz.graph_associations import CORRELATION_ERROR
  from sweetviz.graph_associations import CORRELATION_IDENTICAL
  from functools import cmp_to_key
label_desc: Use image node_id 7 for calling a global function with example usage: create_summary_numeric_group_data(feature_dict, compare_dict) and returns: group_1,  group_2, int, compare_dict